Automated Planning Integrated with Linear Programming Applied in the Container Loading Problem

Cardoso, Rafael M.; Pereira, Bruno; Fonseca, João Paulo da Silva; Ferreira, Marco Vinícius Muniz; Tavares, José Jean-Paul Zanlucchi de Souza

doi:10.3182/20130911-3-br-3021.00077

Cited by 8 publications

(1 citation statement)

References 5 publications

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“…In these researchers, business simulations have been recognized as being one of the fields in which artificial intelligence can benefit immensely. While the interests in the connection of artificial intelligence and business simulation ranks high from the industry practitioners (Clifton & Frohnsdorff, 2001;Cardoso et al, 2013;Ransbotham et al, 2017;Carbonneau, Laframboise & Vahidov, 2008), there still exists the need to explored the contribution of artificial intelligence to the industry. Various studies reviewed such a gap and need (Dubey et al, 2020;Min, 2010;Chong & Bai, 2014).…”

Section: Research Gapmentioning

confidence: 99%

Artificial Intelligence in Business Simulation Analysis

Arshad¹

2020

EJT

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Purpose: Research on business simulation and machine learning has attracted immense interest in the last few years. The aim of this study was to provide a comprehensive view of machine learning in business simulation. To review the use of artificial intelligence in business simulation analysis. A review of the literature, however, shows little systematic reviews on the application of machine learning techniques to business simulation, yet systematic reviews have gained prominence in the academic jargon. Methodology: Thus, this study does reviews systematically a total of 123 shortlisted articles that focus on the machine learning techniques in the business simulation process. Findings: There are immense algorithms of machine learning which can be used in a business simulation, although this study was able to review ten machine learning algorithms in the business simulation process. As a whole, the machine learning algorithms have been deployed to yield lead-time production in the industry. In inventory and storage, machine learning has been applied to improve efficiency in identifying inventory patterns that would have never been revealed and thus saves on costs. Future direction also discussed.

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Section: Research Gapmentioning

confidence: 99%

Artificial Intelligence in Business Simulation Analysis

Arshad¹

2020

EJT

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A Genetic Algorithm Approach to the Automated System for Solving the Container Loading Problem

Cardoso

Ferreira

Sousa

et al. 2016

Communications in Computer and Information Science

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A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

Elahi,

Afolaranmi,

Martinez Lastra

et al. 2023

Discov Artif Intell

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Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/retrofitting. As reported in the 2022 McKinsey Global Survey (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products.

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Automated Planning Integrated with Linear Programming Applied in the Container Loading Problem

Cited by 8 publications

References 5 publications

Artificial Intelligence in Business Simulation Analysis

Artificial Intelligence in Business Simulation Analysis

A Genetic Algorithm Approach to the Automated System for Solving the Container Loading Problem

A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

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